"Factor analysis, a statistical technique, finds application in a range of fields extending beyond its common use in the social sciences. This technique is also relevant in various disciplines:
Psychology: In psychology, factor analysis is a prevalent tool for investigating latent dimensions or factors that account for patterns of correlation among variables. It simplifies intricate data and aids in grasping the organization of psychological constructs.
Economics: Economists frequently employ factor analysis to dissect economic data and pinpoint the underlying factors shaping economic phenomena. It can illuminate the contributors to economic growth, for instance.
Environmental Science: Within environmental science, factor analysis serves to probe relationships among environmental variables and unveil hidden factors that impact environmental processes like pollution levels and climate patterns.
Marketing: Factor analysis is instrumental in marketing research, allowing researchers to comprehend consumer behavior and preferences by unearthing latent factors that influence buying decisions.
Biology: Biologists have harnessed factor analysis for scrutinizing extensive datasets, such as gene expression data, in order to identify patterns and the underlying factors steering biological processes.
Medicine and Healthcare: In the realm of healthcare, factor analysis aids in revealing concealed factors within healthcare data, offering insights into patient outcomes, risk factors for diseases, and the efficiency of healthcare systems.
Education: Educational researchers apply factor analysis to delve into factors that impact student performance, learning outcomes, and the effectiveness of educational programs.
Engineering: Engineers can utilize factor analysis to comprehend intricate relationships among various components within complex systems, facilitating optimization and troubleshooting.
Market Research: Beyond conventional marketing, factor analysis plays a valuable role in market research, encompassing areas such as product development and the analysis of brand perception.
Cognitive Science: Researchers in cognitive science harness factor analysis to delve into the underlying cognitive processes and factors that influence human perception and decision-making."
Exploratory factor analysis (abbreviated as EFA) searches for patterns in correlation matrices, so that more highly correlated sets of variables are combined into different factors. Confirmatory factor analysis (CFA) evaluates the fit of a set of correlations to a pre-specified set of factors.
Exploratory Factor Analysis (EFA) is used to discover underlying factors in data without predefined theories, while Confirmatory Factor Analysis (CFA) tests predefined factor structures based on theory. EFA is data-driven, determines the number of factors statistically, and does not evaluate model fit, whereas CFA is theory-driven, fixes the number of factors, and assesses model fit using fit indices. EFA generates hypotheses, while CFA confirms or disproves theories. Researchers often use EFA first, followed by CFA to refine factor models. Here is a helpful read.
Gorsuch, R. L. (2014). Factor analysis (2nd ed.). Routledge. https://doi.org/10.4324/9781315735740